A typical conventional electric power system includes a transmission system operated by a transmission system operator (TSO) and one or more distribution systems operated by electricity utility companies or distributed system operators (DSOs). The transmission system transmits electric power from generators to the distribution systems via substations. The distribution systems supply the power to loads at consumers, e.g., factories, businesses and homes.
In modern electric power systems, distributed generations (DGs) and demand responsive resources (DRRs) are increasingly common. DG can includes generators powered by solar, wind, landfill gas, and diesel fueled generators. Small generators, such as natural gas fueled micro-turbines can be co-located with consumers.
DRRs change power consumption patterns as a function of price, which can include time-based, critical peak, variable peak, and real time pricing. DRRs can also include comfort level factors, such as reducing power to heating, ventilation, and air conditioning (HVAC) units during peak demand periods. Another comfort level can be ensuring electric vehicle batteries are fully charged when needed. As a characteristic of DG and DRR, the supply and demand for power can vary unpredictably over time.
Typically, the price for the power depends on numerous factors, including the cost to generate and distribute the power, and the needs of the DRR. Typically, a market clearing price and demand for energy (MCPDE) for large geographic regions is set by an open wholesale energy market based on buy and sell bids. In conventional power systems, wherein the supply is primarily pre-scheduled, and the demand is mostly predictable, the MCPDE can be determined days, if not weeks, prior to actual consumption.
However, the flexibility and variations of the DG and the DRR have a major impact on setting the MCPDE, because changes in sunlight and wind, as well as consumer needs tend to be unpredictable. Therefore, in a real-time market for the MCPDE, a response time in the order of seconds may be required.
To optimally use and integrate the supply and demand into the MCPDE in a modern electric power system, two issues need to be solved. The first issue is applying an appropriate pricing mechanism at the distribution level so that the DG and DRR can be awarded according to their specific spatial and temporal contributions. Currently, the TSO-operated wholesale market does not distinguish prices for the loads connected the same substation where all demands are charged at the same averaged price to recover the generation and operation costs. However, this pricing mechanism is unfair for consumers at different locations in power distribution systems. For example, power distributed to remote consumers suffers a greater loss than the power for consumers close to the substations.
The second issue is to optimally integrate the distribution level demand preferences into the transmission network such that the market clearing at the transmission level can account for the impacts of the distribution level. The challenge for the second issue is how to accurately acquire aggregated demand preferences at the distribution level with the participation of the DG and the DRR. However, the demand curves considering the participation of DRR and DG can vary over time due to time varying bidding strategies of the DRR and DG, and therefore the DSO might not obtain the demand curve for all possible scenarios.
Several methods are known for dealing with demand responsive and distributed energy resources in the wholesale energy market. For example, U.S. Pat. No. 8,639,392 describes price quantity bidding from consumers in electricity markets. U.S. Pat. No. 8,554,382 uses a multivariable control approach to provide regulation reserve and demand response to real time correct for the power imbalance. U.S. Pat. No. 8,265,798 achieves congestion reduction by curtailing energy production. U.S. Pat. No. 8,571,955 describes an aggregator based micro-gird incorporating renewable energy resources.
However, all of those methods do not provide an optimal integration of adjustable supply and demands at the TSO and DSO levels.